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6 Insights about Big Data Network Analytics VARs Need to Know

July 28, 2017

When you embrace new technologies like big data and BYOD it adds more moving pieces to network monitoring and management. That’s why more IT departments are applying big data network analytics to help manage the data center itself.

Gartner has identified a new category of big data network analytics tools and services specificly adapted for the data center, IT Operations Analytics (ITOA).Vendors such as HP, IBM, BMC, and Microsoft are already developing ITOA tools using big data to manage the data center. Gartner predicts that ITO will become a central component of enterprise architectures for Global 2000 companies, and that one of every 10 dollars spent in IT Operations Management will be spent on ITOA tools and services.

This opens a new opportunity for VARs seeking new ways to market big data value to their data center customers.

Predictive Analytics Using Big Data

It’s logical that IT should use big data to manage data center problems created by big data. The exploding volume of data types crossing the network is rising at a phenomenal rate; faster than conventional network management tools can handle it. By harnessing big data to power big data network analytics, i.e. ITOA, IT managers can get a better perspective on network traffic patterns and identify concerns before they become problems.

ITOA provides a federated architecture that integrates operational intelligence with IT management tools such as alarm consoles, IT search tools, visualization systems, and forecasting applications. ITOA is especially valuable in six critical areas:

  1. Enhancing application and service modeling
  2. Identifying the root causes of performance problems
  3. Assessing the impact of potential performance problems
  4. Incident anticipation
  5. Capacity planning
  6. Developing network action plans

Real-Time Monitoring Anticipates Network Problems

What makes big data perfect for network analytics is real-time monitoring. ITOA finally offers true, predictive analytics that can identify performance problems before they happen, rather than waiting to report them after the fact.

The advantages of real-time big data network monitoring include:

  • The ability to see the full application stack at a glance
  • The ability to identify and prevent incidents before they happen
  • Shorter response time to incidents using automated responses

Now, rather than generating mountains of statistics after an event occurs, big data network analytics generates a targeted, real-time warning pinpointing the exact problem.

ITOA systems use pattern matching and analytics engines to power complex event processing (CEP), event logs, behavioral learning engines, and architecture discovery and mapping. The capture and analysis of data traffic patterns are used to monitor for pattern deviations. The variables can be very revealing, for example:

  • ITOA can show real-time end user experience to test responsiveness, slow performance, and data availability
  • Thresholds can be set for key performance indicators (KPIs), such as database IOPs, CPU utilization, or number of active connections
  • ITOA delivers an accurate picture of application usage and normal load patterns that can be used as a baseline for enterprise performance

Six Areas Where ITOA Offers Insight

ITOA big data network analytics monitor status, events, and performance at the applications systems layer, monitoring for anomalies that could lead to performance issues, disrupted service, or network failures. The areas where ITOA monitoring seems most valuable are:

  1. Security – ITOA works in tandem with other security tools to identify data theft, malware, and attacks. Using real-time analytics you can intercept fraudulent transactions before they become transaction requests
  2. EndpointsNetwork endpoints can be a weakness in enterprise security, particularly since there are so many to monitor. ITOA solves that problem by monitoring the entire network and automatically isolating any endpoints that seem to be a source of malicious activity
  3. Storage – Big data creates new storage demands requiring enterprise networks to have the elasticity to call on cloud storage and other resources on demand. ITOA can be invaluable in automating management of big data storage capacity
  4. Servers – In a virtualized environment like that used for big data, processing capacity has to have the same elasticity as storage. ITOA can manage virtualized servers to optimize performance
  5. Networks – Using big data network analytics you can get granularity into response times, arrival rates, and other metrics useful for capacity planning without having to add more diagnostic code to core applications
  6. Applications – ITOA provides a central architecture to monitor application performance, identifying root causes for performance problems

Big data for the data center offers a lot of promise in both predicting and automating network problems in a way we never dreamed of using conventional network management tools. Smart resellers are going to understand how to add ITOA to their big data skillset to bring customers to the next level of performance while they reap big data rewards.

Where do you see ITOA fitting into your service offering?